Magnetic resonance locating method

ABSTRACT

The invention relates to a magnetic resonance method for locating interventional devices, in particular in vivo, in which the interventional device bears a marking which in magnetic resonance images influences the measured signals or generates its own measured signals, where the measured signals are processed by means of a one-dimensional signal processing method in order to suppress noise and artefacts. This may in particular be the maximum entropy method, which can be further expanded by the use of model functions. These model functions are subtracted from the measured signals during the iterative method in order in this way to additionally improve the elimination of artefacts. As an alternative to the use of the maximum entropy method, the use of filters, in particular Wiener filters or bandpass filters, is also possible.

The invention relates to a magnetic resonance method for locatinginterventional devices, in particular in vivo, in which theinterventional device bears a marking which in magnetic resonanceacquisitions influences the measured signals or generates its ownmeasured signals.

The use of magnetic resonance methods (MR methods) in medicalinterventions is becoming increasingly important. On the one hand, MRimaging is distinguished by excellent soft tissue contrast and by anyorientation of the image planes; on the other hand, a health risk topatients and operating staff on account of ionizing radiation, as usedin X-ray methods, is avoided.

Nevertheless, when visualizing and locating interventional devices forinsertion into the body of a patient, in particular catheters, there isthe problem that said devices cannot usually be observed directly.Whereas in imaging methods based on the use of X-ray radiation even verysmall metal wires bring about an image contrast sufficient to visualizethe catheter, in magnetic resonance imaging these bring about only aninsufficient signal reduction, since such small objects displace only avery small volume of water. For this reason, the visibility of theinterventional devices must be increased in another way, and variousmethods have been developed for this purpose.

The locating methods described in the literature are subdivided into twocategories. In active methods the interventional device has a receivingcoil so that signals can be received from the surroundings of the devicevia an additional channel. By contrast, passive methods visualize theinterventional device in the MR image by the contrast with respect tothe surrounding tissue.

In the active method sector, two catheter locating methods have thus farbeen established. Firstly, it is possible for a small receiving coil tobe incorporated in the catheter tip, which receiving coil is connectedto a reception channel via a coaxial cable through the catheter (C. L.Dumoulin et al., Magn. Reson. Med. 29, 411-415 (1993)). The greatadvantage of this method is the possibility, by applying fieldgradients, of determining the coordinates of the catheter tip fromprojections in the corresponding spatial directions. Moreover, themethod is compatible with all rapid imaging methods and thus hasreal-time capability.

As an alternative to the use of a receiving coil, it is also possiblefor an elongated antenna to be inserted into the catheter, which antennathen receives MR signals along the catheter. In this way, eveninstruments having a small diameter such as guidewires and neurologicalcatheters can be made visible. One particular field of application is inintravascular imaging.

In both methods it is disadvantageous that the line for HF excitationpulses which runs through the catheter to the reception channel canunintentionally act as an antenna. It has thus been shown that aguidewire can heat up to 74° C. after 30 seconds of a gradient-echosequence. The resonance conditions in this case are varied and inclinical practice are difficult to monitor.

On the other hand there are the passive techniques, in which thevisibility of the catheter is increased in a specific way. Onepossibility is the use of contrast media, with catheters being usedeither whose volume is filled with an appropriate medium (Gd-DTPA) orwhose sheath is coated in a contrast-amplifying manner.

Another approach consists in generating susceptibility artefacts in theMR image by disturbing the static magnetic field B₀. Conventionalpolyethylene catheters may for this purpose be prepared withparamagnetic rings (Dy₂O₃). A working group at the University Clinic ofRWTH Aachen has developed an alternative method in which a local fieldinhomogeneity is brought about by a wire loop in the catheter, whichwire loop is then connected to an external power source (A. Glowinski etal., Magn. Reson. Med. 38, 253-258 (1997)). In this way, the imageartefact can be controlled via the source during the intervention.

In these three passive visualization techniques, the positive aspectsare that it is possible to make the entire length of the cathetervisible and that the methods are compatible with all imaging methods.The disadvantages are that all methods are comparatively time-consumingand the coordinates of the catheter position-are not directlyaccessible. Automated tracing of the catheter is therefore not possible.

According to another locating method described by M. Burl, Magn. Reson.Med. 36, 491-493 (1996) and S. Weiβ, Proc. ISMRM, 544 (2001), acatheter, also referred to as an OptiMa catheter, is fitted at its tipwith an electronically isolated resonant circuit which is tuned to theLarmor frequency. When a B₁ HF pulse is transmitted, the resonantcircuit is excited and causes a local increase in resonance of the B₁field, which locally increases the flip angle and thus the signal. Theresonant circuit can be detuned optically by way of a photodiode whichis illuminated by a lightguide running through the catheter, and hencethe signal amplification can be turned on and off. The signal backgroundis suppressed by subtracting an on/off signal. The measured signalsobtained when the marking is activated and deactivated are also referredto as on-projection and off-projection, respectively.

This method is distinguished in that the catheter coordinates aredirectly accessible and the technique is compatible with all imagingmethods. Patient safety is also ensured since a lightguide runningthrough the catheter, unlike an electrical guide, cannot act as anantenna which heats up considerably under the effect of HF pulses.Finally, the method also has real-time capability.

However, one disadvantage in this prior art is that the detection of theinterventional device is not ensured in every case, since thedetermination of the coordinates can be disrupted by noise andartefacts. The position of the device is determined from the differencebetween on-projection and off-projection by the sampled value withmaximum signal amplitude. However, the signal quality is adverselyaffected by various effects. Firstly, the quality of the signal ishighly dependent on the distance between the receiving coil and themarking on the interventional device, since the pulse is weaker thefurther the receiving coil is from the origin of the signal.Nevertheless, the signal quality is affected to a much greater extent bythe orientation of the device with respect to the transmitting andreceiving coil. When there are large angles between the resonant coil,locally approximated by a dipole moment, and the field lines of thetransmitting and receiving coil, these couple only to a weak extent.

Apart from the high degree of variation in the pulse brought about bythe interventional device, the location operation is significantlydisrupted by extended artefacts. Frequently, the background signal inthe difference is not fully extinguished, and this can be attributed tothe fact that the magnetization, at the moment of excitation for therespective projections, is not in the same state but rather is subjectedto a transient process. For this reason, the amplitudes in theon-projection and off-projection are at different levels. The artefactsbrought about in this way will be referred to herein below as transientartefacts.

Further artefacts, also referred to as image slice artefacts, arisesince in each new detection the magnetization in the previous imageslice has generally not fully died out. This residual magnetization thendies out between on-projection and off-projection and therefore appearsin the difference projection as an artefact in the center of the datavector. Finally, movements caused by breathing and the heartbeat andalso pulsed blood flow may have a negative effect on the quality of thesignal.

A reliable conclusion about the position of the interventional devicescan no longer be drawn if the background, caused by noise and artefacts,of the amplitude of the pulse emanating from the marking of theinterventional device gets closer. Based on this prior art, it istherefore an object of the present invention to provide a magneticresonance method for locating interventional devices, in which noise andartefacts are suppressed to the extent that the detectability of thesignal coming from the marking of the interventional device is alwaysensured.

The object is achieved according to the invention by a magneticresonance method as claimed in the precharacterizing part of claim 1, inwhich the measured signals are processed by means of a one-dimensionalsignal processing method in order to improve the location operation.

Furthermore, the invention also relates to an apparatus and to acomputer program for carrying out the method according to the invention.

In the context of this invention, the term interventional device isunderstood to mean in particular catheters, but also biopsy needles,minimally invasive surgical instruments, guidewires, stents, etc. Themarking on the interventional device may in particular be a resonantcircuit at the tip of an OptiMa catheter; however, it may also be othertypes of arrangement such as, for example, a microcoil as used foractive locating methods. A marking which can be switched on and off,allowing the separate recording of measured signals in the on and offstate, also referred to in the context of this invention ason-projection and off-projection, is advantageous here, so that theposition determination of the marking is possible by differenceformation between on-projection and off-projection.

The one-dimensional signal processing method is preferably an iterativemethod as provided for problems which cannot be solved directly byanalysis. The so-called maximum entropy method is particularly suitable.

The maximum entropy method (ME method) is an iterative, nonlinear methodfor signal restoration. The ME method solves underdefined problems byselecting, from all the solutions that are compatible with the data,that solution having the maximum entropy. One particular advantage isgiven by the possibility of taking into account prior knowledge aboutthe measuring process by including additional parameters in thealgorithm.

The initial problem on which the maximum entropy method is based can bedescribed in general terms as follows:

The object is to determine a distribution function as the best estimatefor a distribution of states. Usually there are an infinite amount ofdistributions which are compatible with the secondary conditions. Theprinciple of maximum entropy means that from these, that distributionwhich has the maximum entropy is to be selected. This choice is the onlyone that is consistent with the data without adding additionalinformation.

One approach, based on probability theory, for substantiating the MEmethod is described inter alia by G. J. Daniell and S. F. Gull in IEEProc. 127, Pt. E, 170-172 (1980). This states that the following is truewhen the input signal is superposed by white noise:$\chi^{2} = {\sum\limits_{signal}\frac{\left( {{deviation}\quad{between}{\quad\quad}{measured}{\quad\quad}{signal}\quad{and}{\quad\quad}{forecast}{\quad\quad}{signal}} \right)^{2}}{\left( {{error}{\quad\quad}{in}\quad{measured}{\quad\quad}{signal}} \right)^{2}}}$

The probability for the estimated signal is then proportional toexp(−½χ²). The ME method is thus based on a χ² minimization withadaptation of the estimated signals to the measured data. The algorithmwhich is attributed to the authors Skilling and Bryan, Mon. Not. astr.Soc. 221, 111-124 (1984) and is distinguished by a high convergence ratehas proven to be particularly suitable for use in the method accordingto the invention.

According to an advantageous design of the invention, to suppressartefacts occurring in the measured signals, model functions are formed,adapted and subtracted from the measured signals as the iterative methodis carried out. The adaptation of the model functions to the recordedmeasured signals (the on-projection) expediently takes place by themodel, functions being calculated with a scaling parameter. Theincorporation into the maximum entropy algorithm can take place in twodifferent ways. The scaling parameter can be adapted anew after eachiteration step or just once prior to the ME iteration. In the testcarried out for this purpose, in the first case the parameter wasdetermined as a function of noise with an accuracy from 1 to 4%, whereasin the second case the relative deviation was approximately twice asgreat. On the other hand, in the second case approximately 10% lesscalculation time was required.

In the case of the artefacts that are to be eliminated, a distinctionmust be made, as already mentioned above, between transient artefactsand image slice artefacts Since the occurrence of transient artefactscan be attributed to the fact that the magnetization at the time ofexcitation, for measurements in which the marking on the interventionaldevice is switched on and off, is not in the same state, in particularwhen using the abovementioned OptiMa catheter which has a marking thatcan be switched optically, the background signal is not completelyextinguished by forming the difference of measurements with activatedand deactivated marking.

For this reason, a recorded off-projection can be used as model functionto suppress the transient artefacts. By way of the abovementionedscaling parameter, the model function created in this way can be adaptedto the recorded measured signals, by the on-projection andoff-projection being compared with one another. During the χ² adaptationthe model function is then subtracted from the measured signal. Thesignal defining the position of the interventional device is thusamplified relative to the background, so that the sampled value with themaximum signal amplitude can be assigned to the position withconsiderably increased certainty.

By contrast, in order to suppress the image slice artefacts which mayalso occur and which can be attributed to the fact that in theindividual detections the magnetization in the previous image slice hasgenerally not completely died out, other model functions must be used.In this case, rectangular or Gaussian functions may be used, which canlikewise be adapted by way of a scaling parameter. The reason for thetype of model function used can be seen in the considerably narrowerimage of the image slice artefacts, which are of the order of magnitudeof the width of an image slice, compared to transient artefacts.

In order to be able to draw a conclusion about the capability of signalprocessing relative to the quality of the input signal, two differentparameters are used. Firstly, the signal-to-noise ratio S/N providesinformation about the noise minimization following signal processing,although no account is taken of any signal interference on account ofartefacts which under some circumstances impair the determination of theposition of the interventional device much more than noise alone. Moreinformation is thus provided by the signal-to-interference ratio S/A,which besides the high frequency noise also takes the low frequencyartefacts into account. These are the quotients of the useful signalpower and the total power reduced by the power of the DC signal. Whenthe noise in a signal is dominant, the S/A strives againist the S/Nratio. The suppression of noise alone however, only leads to a slightimprovement in the S/A ratio. The S/A ratio is much more suited than theS/N ratio to assess the certainty with which the position is determined.Thus, in the investigations carried out, it has been found that there isa reliable detectability of the position of the interventional devicewhen an S/A ratio of ≧20 dB is measured.

The convergence rate of the maximum entropy algorithm is primarilydependent on the noise. Independently thereof, the number of iterationscan be influenced by a suitable choice of the user-defined background,that is to say of the start value of the iteration, since the success ofthe χ² adaptation at the start of the iteration varies depending of thechoice of this start value. An increase in the convergence rate isparticularly important when signal processing in real time is desired.

It has been found that in the method according to the invention, withoutadditional use of model functions, the convergence rate is at a maximumwhen the mean value of the measured signals is selected as the startvalue for the iteration. At the same time, the maximum S/N ratio is alsoobtained for this choice of the user-defined background, whereas the S/Aratio is largely independent of the choice of start value for theiteration. Given a suitable choice of start value, the ME algorithmconverges in less than ten iteration steps. If, on the other hand, modelfunctions in accordance with what has been stated above regarding theoptimization of the signal processing and elimination of artefacts areused, it has been found to be expedient to use the mean value of thedifference between measured signals and model function as start valuefor the iteration. This mean value is considerably less than the meanvalue of the measured signal, since the significant artefacts havealready been suppressed by the model function.

A further possibility for increasing the quality of the measured signalsthat is offered by the maximum entropy method consists in suppressingnoise and artefacts by extinguishing the corresponding high frequency orlow frequency input signal fractions. Since the reliable determinationof the position of the interventional device is impaired to a greaterextent when there are extended artefacts having a high amplitude than bynoise alone, it is particularly important to suppress said artefacts.Both in vitro and in vivo, artefacts which were four to five times widerthan the pulse emanating from the marking were usually observed. Given atotal number N of 256 sampled values, these are typically artefactswhich extend over more than 32 sampled values.

The suppression of an unnecessarily large amount of signal fractionsnevertheless leads to losses in the S/N ration, and this can beattributed to the fact that by extinguishing these low frequency signalfractions the mean value is significantly decreased while the noiseessentially remains unaffected. Accordingly, for example given anartefact width of 32 sampled values, the S/A ratio is at a maximum when8 low frequency sampled values are eliminated, and this corresponds tothe quotient of the total number of sampled values and the number ofsampled values across which one artefact extends. Moreover, theextinguishing of too many low frequency signal fractions which contain alot of signal power when massive artefacts occur may lead to theconvergence criteria for the ME algorithm no longer being fulfilled iftoo low a start value is used for the iteration.

An improvement in the signal quality by eliminating noise and thus animprovement in the S/N ratio may be obtained by extinguishing highfrequency sampled values in the spectrum. The extinguishing of too manyhigh frequency sampled values nevertheless leads to a significantdecrease in the useful signal power, which is associated with losses inthe S/A ratio. Given a total number of N=256 sampled values, it wasfound that no more than 96 high frequency sampled values should beextinguished, since in this range the spectrum of the useful signal isnegligible. A significant effect on the number of iteration steps bysuppressing high frequency or low frequency signal fractions and henceon the calculation time could not be established.

In vivo experiments, it was possible to show that reliable positiondetermination is possible by eliminating signal fractions even whenthere are input signals that contain a lot of noise and are highlydisrupted by artefacts. However, it must be pointed out that in theexpanded ME method described above, in which adapted model functions aresubtracted from the measured signals, the elimination of sampled valuesis not useful. This can be attributed to the fact that during the χ²adaptation the artefacts corresponding to the model function aresubtracted from the measured signal, with it being necessary for theestimated signal to be brought into correspondence with this differencesignal. An additional extinguishing of low frequency signal fractionswould therefore lead to a falsification, which no longer permitsadaptation.

Besides the iterative methods described above, particularly the maximumentropy method, it is also possible to use other one-dimensional signalprocessing methods such as, for example, filters. In principle, bothfilters having a finite impulse response and filters having an infiniteimpulse response are suitable, these also being referred to by the termsFIR (finite impulse response) and IIR (infinite impulse response). Suchfilters are known in principle to the person skilled in the art. Twotypical filters which have been found to be suitable for achieving theobject of the invention are the Wiener filter and the bandpass filter.

The Wiener filter can be depicted in Fourier form as follows:$W = {\frac{1}{H}*\frac{\Phi_{ff}{H}^{2}}{{\Phi_{ff}{H}^{2}} + \Phi_{nn}}}$

In this case, H is the transfer function of the measurement system and(Φ_(ff) and Φ_(nn) are the power density spectra of the sought-aftersignal f_(k) and noise n_(k).

The Wiener filter is particularly suitable for improving the S/N ratio,that is to say for effectively suppressing noise. Artefacts, on theother hand, are suppressed to a poorer extent than when the maximumentropy method is used.

A further suitable filter is the bandpass filter which has proven to beeffective for suppressing noise and artefacts. The certainty with whichan interventional device can be located could be considerably increasedwith the aid of a bandpass filter. The bandpass filter is less suitableonly in the case of suppressing narrow artefacts, such as image sliceartefacts for example.

The choice of the most suitable signal processing method depends on theexact nature of the problem. On the one hand, the maximum entropy methodgives the best results in terms of artefact and noise suppression,particularly when implementing the additional features mentioned above.On the other hand, the ME method, as an iterative method, requiresconsiderably more calculation time than when a filter is used. Whilesaid calculation time is in the range from 1 to 2 ms for a filter, forthe ME method the calculation time may be >100 ms, depending on thetotal number of sampled values. Therefore, when there are very strictrequirements in terms of the brevity of the calculation time forreal-time visualization, a filter should be used instead of the MEmethod.

A further improvement in the location of an interventional device can beachieved, when there are a number of measured signals being used forlocating purposes, in that after processing of the measured signals bymeans of the one-dimensional signal processing method a check as tocoincidence of the positions of the interventional device determined byway of the processed measured signals is carried out. Such a check isprovided in particular when using the above-described OptiMa catheter,in which case a number of receiving coils which receive the measuredsignals in parallel are located on the body of the patient. Althoughthese measured signals differ from one another during the locationoperation in terms of the amplitude, the same position in terms of spaceshould be obtained for the interventional device.

When checking the processed measured signals with regard to coincidence,after processing of the measured signals a check is then made as towhether the positions determined via the individual receiving coilscoincide. Such a full or partial coincidence additionally increases theprobability that the determined position is correct.

Preferably, the various measured signals being used to locate theinterventional device are processed jointly in the one-dimensionalsignal processing method, so that the effects on the positiondetermination for the individual measured signals are also the same.This is possible both by using an iterative method such as the maximumentropy method and by using a filter. The determined positions for theinterventional device can then be checked with regard to coincidence.The correlation of the measured signals can also be calculated directlyby the one-dimensional signal processing method in order in this way toobtain a measure of the coincidence of the signal spectra.

The invention will be further described with reference to examples ofembodiments shown in the drawings to which, however, the invention isnot restricted.

FIG. 1 shows the signal amplitudes plotted against the sampled values toillustrate the signal restoration using the expanded ME method in theevent of strong interference of the input signals by transientartefacts.

FIG. 2 shows the signal amplitudes plotted against the sampled values toillustrate the signal restoration using the expanded ME method in theevent of strong interference of the input signals by image sliceartefacts.

FIG. 1(a) shows an in vitro input signal having a total number of N=256sampled values, in which the catheter position is marked by an arrow.The signal amplitudes on the ordinate are shown in graph form on theabscissa for the individual sampled values. The measurements were takenby means of a 1.5 Tesla MR tomography scanner (GyroScan ACS-NT, PhilipsMedical Systems) using a “spoiled” gradient-echo sequence (FOV=256 mm),where the catheter, which is an OptiMa catheter, has been placed in atube phantom. The input signal is highly disrupted by transientartefacts, which are eliminated by forming and adapting a model functionthat is subtracted from the measured signals during the ME method. Themodel function used is the off-projection shown in (b), and this showsthe recorded signals when the marking on the catheter is deactivated.The result after signal restoration has been completed is shown in (c),and the unambiguous determinability of the catheter position can beclearly seen here. The signal processing is associated with aconsiderable rise in the S/N and S/A ratios. Similarly, illustrations(d)-(f) show the signal restoration of an in vivo input signal which ishighly disrupted by transient artefacts, where in this case the totalnumber of sampled values was N=128. FIG. 1(d) in this instance shows theinput signal, (e) shows the corresponding off-projection and (f) showsthe result after signal restoration has been completed. The same methodwas used for the in vivo measurements as for the in vitro measurements,although in the case of the in vivo measurements an appropriate catheterwas inserted into the aorta of a pig and a refocused gradient-echosequence (FOV=300 mm) was used.

FIG. 2(a) shows a catheter signal with narrow image slice artefacts,where once again the signal amplitudes are shown in graph form for theindividual sampled values and the position of the catheter is shown byan arrow. The model functions used within the context of the expanded MEmethod, which in the iterative method are again subtracted from measuredsignals, are shown in (b). In (c) it can be seen that after signalrestoration the position of the catheter can be determinedunambiguously, even though the artefacts occurring in (a) are verynarrow and exceed the true catheter position in terms of amplitude.

1. A magnetic resonance method for locating interventional devices, inparticular in vivo, in which the interventional device bears a markingwhich in the magnetic resonance acquisition influences the measuredsignals or generates its own measured signals, wherein the measuredsignals are processed by means of a one-dimensional signal processingmethod.
 2. A method as claimed in claim 1, wherein the one-dimensionalsignal processing method is an iterative method.
 3. A method as claimedin claim 2, wherein the iterative method is based on the maximum entropymethod.
 4. A method as claimed in claim 2, wherein, for artefactsoccurring in the measured signals, model functions are formed, adaptedand subtracted from the measured signals as the iterative method iscarried out.
 5. A method as claimed in claim 4, wherein the modelfunctions are adapted to the recorded measured signals by way of ascaling parameter.
 6. A method as claimed in claim 5, wherein the modelfunctions are adapted anew to the recorded measured signals after eachiteration step in the iterative method.
 7. A method as claimed in claim5, wherein the model functions are adapted to the recorded measuredsignals once, before the iterative method is carried out.
 8. A method asclaimed in claim 4, wherein the measured signals recorded when themarking on the interventional device is inactive are used as modelfunction.
 9. A method as claimed in claim 4, wherein rectangular orGaussian functions are used as model functions.
 10. A method as claimedin claim 4, the mean value of the difference between measured signal andmodel function is selected as start value for the iteration.
 11. Amethod as claimed in claim 2, wherein the mean value of the measuredsignal is selected as start value for the iteration.
 12. A method asclaimed in claim 1, wherein high and/or low frequency signal fractionsare eliminated in order to suppress noise and/or artefacts in therecorded measured signals.
 13. A method as claimed in claim 1, wherein afilter with a finite or infinite impulse response is used asone-dimensional signal processing method.
 14. A method as claimed inclaim 13, wherein the filter is a Wiener filter or a bandpass filter.15. A method as claimed in claim 1, wherein during the evaluation of anumber of measured signals being used to locate the interventionaldevice, after processing of the measured signals by means of theone-dimensional signal processing method a check as to coincidence ofthe positions of the interventional device determined by way of theprocessed measured signals is carried out.
 16. A method as claimed inclaim 1, wherein a number of measured signals being used to locate theinterventional device are processed jointly in the one-dimensionalsignal processing method.
 17. A method as claimed in claim 1, whereinthe measured signals are recorded in parallel by a number of receivingcoils.
 18. A method as claimed in claim 1, wherein the one-dimensionalsignal processing method calculates the correlation of one or moremeasured signals.
 19. An apparatus for locating interventional deviceswith the aid of magnetic resonance acquisition, in which theinterventional device bears a marking which in the magnetic resonanceacquisition influences the measured signals or generates its ownmeasured signals, wherein the apparatus has program control for carryingout a method as claimed in claim
 1. 20. A computer program forprocessing measured signals during the location of interventionaldevices with the aid of magnetic resonance acquisition, in which theinterventional device bears a marking which in the magnetic resonanceacquisition influences the measured signals or generates its ownmeasured signals, wherein a method as claimed in claim 1 can be carriedout by means of the computer program.